Multistage Spectrum Sensing for Cognitive Radios

transcript

UCLA CORES

Outline

1. Introduction

2. Problem Statement

3. Proposal

4. Markov Chain Model

5. Results

Spectral Vacancy

Frequency

Spectral Vacancy

Introduction to Spectrum Sensing

The Frequency Spectrum is mostly allocated Spectral vacancies exist in:

1. Unallocated frequency bands.

2. Allocated bands where the Primary Users (PUs) are spatially absent or temporarily idle.

Cognitive radios (CRs) find spectral vacancies by performing spectrum sensing. Spectrum Sensing Design Objectives

1. Maximize the CR throughput

2. Minimize delay in vacating channel for an incoming PU

3. Minimize collisions between CRs and PUs

Spectral Vacancy

Cognitive Radio

OSI Model of Cognitive Radios

Collaborative Sensing

Application

BandwidthMAC

Sensing Method

Design Parameters:

1. Sensing Algorithm

2. Sensing Time

Design Parameters:

1. Narrowband

2. Wideband

PU Traffic

Conventional Single Stage Sensing

i. No PU

ii. PU arrives

Problem Statement

CR Active Sensing CR Active Sensing CR Active Sensing

CR Active Sensing

FalseAlarm (FA)

-Throughput Waste-

CR Stops

Transmission

CR Active Sensing

-Collision with PU--Delay in detecting PU-

No Misdetection

(MD) CR Stops

Transmission

0 T 2T 3TS S S

Multistage Sensing

i. No PU

ii. PU arrives

Degrees of freedom:1. Number of Sensing Stages (S)

2. Sensing Methods

3. Sensing Times

CR ActiveSensingStage 1

FA CR Stops

Transmission

CR ActiveSensingStage S

S1 S2 SS

No MD CR Stops

Transmission

CR ActiveSensingStage S

S1 S2 SS

Previous Work

802.22 features 2-stage sensing: Coarse and Fine sensing.

Jeon et al* propose a multistage sensing algorithm* W. S. Jeon, D. G. Jeong, J. A. Han, G. Ko, and M. S. Song, "An efficient quiet period management scheme for cognitive

radio systems," IEEE Trans. Wireless Comm., vol. 7, no. 2, Feb. 2008.

Limited model; single channel, single sensing algorithm, no collaboration, simple traffic model.

Literature lacks a unified analytical framework that includes Multistage Sensing

ProposalWe introduce a unified analytical framework that models:

Multistage Sensing Number of sensing stages

Sensing Methods Algorithm Sensing Time

Bandwidth Narrowband Wideband

CR traffic models CBR – VBR Buffered – Unbuffered

Goal is to analyze the impact of varying parameters on:

1. CR throughput

2. Delay in vacating channel for a PU

3. CRs and PUs collisions

Discrete Time Markov Chain

Analysis is based on Markov Chain:Well established Math tool for modeling discrete space

stochastic processesFuture evolution of process depends solely on current

state.Provides closed form/numerical solutions for steady

state probabilities and process variables

Assumptions

1. PUs and CRs arrive and depart at discrete times that are multiples of T

2. CRs communicating together are synchronized through a control channel

3. Time taken to switch between communicating and sensing modes is negligible

Model OverviewModel is divided into 3 levels:

1.CR traffic level

2.Multistage sensing level

3.Spectrum Sensing level

ImplementationLevel 1 – CR Traffic Level

CR IdleCR Sensing

and/or

TransmittingPCR

1 - PCR

1 - QCR

PCR ≡ Probability of arrival of a CR

QCR ≡ Probability of departure of a CR

PCR and QCR are tuned to accommodate for different traffic models: CBR – VBR Buffered – Unbuffered

(Buffer Size)

ImplementationLevel 2 - Multistage Sensing Level

CR Sensing

and/or

Transmitting

S ≡ Number of Sensing Stages

CR Sensing

and/or

TransmittingCR Idle

1 - PCR

1 - QCR

CR Active

Stage 1

CR Active

Stage 2

CR Active

Stage SCR Quiet

PU detected

or False Alarm

PU misdetected

or no False Alarm

PU detected

or False Alarm

CR Active

Stage i

ImplementationLevel 1 – CR Traffic Level

ImplementationLevel 3 – Spectrum Sensing Level

CR Active

Stage 1

PU Absent

CR Active

Stage i

PU Absent

CR Active

Stage i + 1

PU Absent

CR Active

Stage 1

PU Present

CR Active

Stage I

PU Present

CR Active

Stage i + 1

PU Present

(1-PPU).Pi

(1-QPU).(1-Qi)

QPU.Pi

PPU.(1-Qi)

(1-PPU).(1-Pi)

(1-QPU).QiPPU ≡ Prob of arrival of a PU

QPU ≡ Prob of departure of a PU

Pi ≡ Prob of False Alarm at Stage i

Qi ≡ Prob of Misdetection at Stage i

PPU and QPU reflect the PU traffic model

Pi and Qi are tuned to describe the sensing method

CR Active

Stage i

CR Active

Stage i

CR Active

Stage 1

CR Active

Stage 2

CR Active

Stage SCR Quiet

ImplementationLevel 2 - Multistage Sensing Level

Implementation – BandwidthNarrowband Sensing, N Channels

Implementation 1: Implementation 2:

CR QuietCR ActiveSensingStage 1

Example: Single stage, 3 channels:

CR QuietCR ActiveSensingStage 1

Implementation – BandwidthNarrowband Sensing, N Channels

Throughput for the Narrowband Case

Simulation Configuration

Delay in finding PU

Implementation – BandwidthWideband Sensing

IDLE Tx

1-P 1-P

N Channels

CR ActiveSensing

Stage 1CR Active

Sensing

Stage 2CR Active

Sensing

Stage 3CR Active

Sensing

Stage 4

CR ActiveSensing

Stage 3CR Active

Sensing

Stage 1CR Idle CR Active

Sensing

Stage 1

CR ActiveSensing

Stage 1CR Active

Sensing

Stage 1CR Quiet CR Quiet

Throughput for the Wideband Case

Appendix

AWGN independent channels. Sensing time = , where i is the stage number, L0 = 50 us, Ts = 20 ms, and δ =

2. Sensing time for the last sensing stage = Ts.

Energy Detection parameters: Pr = -104 dBm.

Noise Floor = -163 dBm. BW = 6 MHz SNR = -8.8 dB. Energy Threshold = -94.74 dBm.

# of Sensing Nodes = 5.

SU Arrival Probability = 0.2. SU Departure Probability = 0.2. PU Arrival Probability = 0.05. PU Departure Probability = 0.05.

Switching time = 0.2xTs. 2 Channels 1000 stages. 10,000 cycles.

},min{ 0LTs i

Multistage Spectrum Sensing for Cognitive Radios

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